Analysis of merged questionnaires

1 Plan that I wrote with Richi’s comments

Link at https://docs.google.com/document/d/1bdNeOMAYY90k8FAbPRWGBgS1YBEdP09kP0vcW0tNgPc/edit?usp=sharing

2 Read in data

european <- read_csv("01-cleaning_data_data/european_recoded.csv")
australian <- read_csv("01-cleaning_data_data/australian_recoded.csv")

dim(european)

[1] 209 117

dim(australian)

[1] 269 146

european$EU <- 1
australian$AU <- 1

all <- merge(european,australian,all = TRUE)

table(all$EU,all$AU,useNA = "always")

      
         1 <NA>
  1      0  209
  <NA> 269    0

2.1 Variables to describe dataset (can be different between contexts)

demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
l2School <- "\\.L2school$"
l2School_variables <- colnames(all)[grep(l2School,colnames(all))]

• First language

#table(all$L1,all$Context) # too many levels - needs to be cleaned (ex tot number of languages?)
table(all$L1,useNA = "always")

         Afrikaans           Albanian            Burmese          Cantonese            Chinese           Croatian              Dutch 
                 1                  2                  1                  4                  7                  1                  1 
           English  English and Dutch             German German and English German and Turkish                  I         Indonesian 
               201                  2                 77                  2                  1                  1                  1 
           Italian           Japanese           Mandarin            Persian    Persian (Farsi)           Romanian            Russian 
                89                  1                  5                  1                  1                  3                  2 
            Sindhi             Slovak            Spanish            Turkish          Ukrainian               <NA> 
                 1                  1                  3                  1                  1                 67 

ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="")+theme_bw()

#table(all$speak.other.L2,all$Context)

L2 <- data.frame(Freq=table(all$speak.other.L2)[order(table(all$speak.other.L2),decreasing = TRUE)],
                 L2=names(table(all$speak.other.L2))[order(table(all$speak.other.L2),decreasing = TRUE)]) # too many levels - needs to be cleaned (ex tot number of languages?)
head(L2)

• origins

table(all$origins,useNA = "always")

  No  Yes <NA> 
 323   89   66 

table(all$year.studyL2)

                     0 years                   1- 3 years                    1-3 years                    4-6 years 
                          69                           12                           11                           61 
First year of primary school                 Kindergarten             Less than a year            more than 6 years 
                          80                           30                           27                           49 
                       Other 
                          72 

table(all$degree)

                    BA in Anglistik            BA in Nordamerikastudien                                 HUM 
                                 43                                   4                                 129 
                            HUM.SCI                                  LA      Lingue e letterature straniere 
                                  6                                  36                                  82 
Lingue, mercati e culture dell'Asia                                  QC                                 SCI 
                                 13                                   5                                  86 

• study.year in the European context is uni1.year in the Australian context

all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
#table(all$study.year)
all$study.year <- ifelse(all$study.year == "Already graduated after 5 semesters in March 2016, was interested in survery/study, sorry.","6th semester",all$study.year)
table(all$study.year)

      1st semester           1st year       2nd semester           2nd year       3rd semester           3rd year 3rd year of Master 
                72                255                  5                 37                  5                 20                  1 
 4th year bachelor       5th semester       6th semester           7th year             Master 
                 8                  2                  3                  1                  3 

• proficiency

table(all$prof,useNA = "always")

          Advanced         Elementary       Intermediate Upper-intermediate               <NA> 
                78                105                 84                146                 65 

3 Filter participants : keep only the ones that meet the inclusion criteria

all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
# Filter only subject that we want to include in the study
# names(table(all$study.year))[1] = 1st semester"

filtered <- subset(all, (study.year == "1st year") | (study.year == names(table(all$study.year))[1]))
#& year.studyL2 != "0 years"

3.1 Imputing degree based on Quality Comments provided in the questions

• 5313976716 : SCI (wish to study science in the future)
• 5359866545 : HUM.SCI (based on degree.other7)
• 5375370122 : SCI (she wants to do science)
• 5375376761 : HUM (she wants to do translation/reserach/teaching)

filtered$degree[filtered$Resp.ID %in% "5313976716"] <- "SCI"
filtered$degree[filtered$Resp.ID %in% "5359866545"] <- "HUM.SCI"
filtered$degree[filtered$Resp.ID %in% "5375370122"] <- "SCI"
filtered$degree[filtered$Resp.ID %in% "5375376761"] <- "HUM"

table(filtered$degree)

                    BA in Anglistik            BA in Nordamerikastudien                                 HUM 
                                 39                                   4                                  98 
                            HUM.SCI                                  LA      Lingue e letterature straniere 
                                  6                                  27                                  78 
Lingue, mercati e culture dell'Asia                                 SCI 
                                 13                                  58 

kable(table(filtered$Context))

Var1	Freq
English in Germany	72
English in Italy	91
German in Australia	89
Italian in Australia	75

kable(table(filtered$year.studyL2,filtered$Context))

	English in Germany	English in Italy	German in Australia	Italian in Australia
0 years	0	0	22	11
1- 3 years	0	0	0	9
1-3 years	0	0	7	0
4-6 years	0	0	35	20
First year of primary school	17	56	0	0
Kindergarten	6	23	0	0
Less than a year	0	0	13	5
more than 6 years	0	0	11	30
Other	48	12	0	0

kable(table(filtered$year.studyL2,filtered$prof))

	Advanced	Elementary	Intermediate	Upper-intermediate
0 years	0	32	1	0
1- 3 years	0	4	5	0
1-3 years	0	1	3	3
4-6 years	1	9	26	19
First year of primary school	20	1	6	46
Kindergarten	8	1	3	17
Less than a year	0	11	4	3
more than 6 years	2	5	16	18
Other	33	0	5	22

kable(table(filtered$year.studyL2,filtered$Context))

	English in Germany	English in Italy	German in Australia	Italian in Australia
0 years	0	0	22	11
1- 3 years	0	0	0	9
1-3 years	0	0	7	0
4-6 years	0	0	35	20
First year of primary school	17	56	0	0
Kindergarten	6	23	0	0
Less than a year	0	0	13	5
more than 6 years	0	0	11	30
Other	48	12	0	0

People that have studied 0 years L2 are just a small subset of the German in Australia and Italian in Australia context which means that by correcting for context we are not removing the effect of the 0 years. A way to remove the effect of the 0 years participants and not including too many variables could be to estimate the effect of 0 years vs all.

4 Define demographic variables

4.1 Need to check datasets with Rcihi (why do we have QC in L1? Am I using not the latest dataset?)

all <- filtered
demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
l2School <- "\\.L2school$"
l2School_variables <- colnames(all)[grep(l2School,colnames(all))]

ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="") + theme_bw()

table(all$L1,all$Context)

                    
                     English in Germany English in Italy German in Australia Italian in Australia
  Afrikaans                           0                0                   1                    0
  Albanian                            0                1                   0                    0
  Cantonese                           0                0                   2                    0
  Chinese                             0                2                   2                    0
  Dutch                               1                0                   0                    0
  English                             1                0                  75                   73
  English and Dutch                   0                0                   2                    0
  German                             64                0                   0                    0
  German and English                  1                0                   1                    0
  I                                   0                0                   0                    1
  Indonesian                          0                0                   1                    0
  Italian                             0               87                   0                    0
  Japanese                            0                0                   1                    0
  Mandarin                            0                0                   1                    1
  Persian (Farsi)                     0                0                   1                    0
  Romanian                            0                0                   1                    0
  Russian                             2                0                   0                    0
  Sindhi                              0                0                   1                    0
  Spanish                             1                0                   0                    0
  Turkish                             1                0                   0                    0
  Ukrainian                           0                1                   0                    0

table(all$degree,all$L1)

                                     
                                      Afrikaans Albanian Cantonese Chinese Dutch English English and Dutch German German and English  I
  BA in Anglistik                             0        0         0       0     0       1                 0     34                  1  0
  BA in Nordamerikastudien                    0        0         0       0     0       0                 0      4                  0  0
  HUM                                         1        0         2       0     0      93                 1      0                  0  1
  HUM.SCI                                     0        0         0       0     0       6                 0      0                  0  0
  LA                                          0        0         0       0     1       0                 0     25                  0  0
  Lingue e letterature straniere              0        1         0       1     0       0                 0      0                  0  0
  Lingue, mercati e culture dell'Asia         0        0         0       1     0       0                 0      0                  0  0
  SCI                                         0        0         0       2     0      47                 1      0                  1  0
                                     
                                      Indonesian Italian Japanese Mandarin Persian (Farsi) Romanian Russian Sindhi Spanish Turkish
  BA in Anglistik                              0       0        0        0               0        0       2      0       1       0
  BA in Nordamerikastudien                     0       0        0        0               0        0       0      0       0       0
  HUM                                          0       0        0        0               0        0       0      0       0       0
  HUM.SCI                                      0       0        0        0               0        0       0      0       0       0
  LA                                           0       0        0        0               0        0       0      0       0       1
  Lingue e letterature straniere               0      75        0        0               0        0       0      0       0       0
  Lingue, mercati e culture dell'Asia          0      12        0        0               0        0       0      0       0       0
  SCI                                          1       0        1        2               1        1       0      1       0       0
                                     
                                      Ukrainian
  BA in Anglistik                             0
  BA in Nordamerikastudien                    0
  HUM                                         0
  HUM.SCI                                     0
  LA                                          0
  Lingue e letterature straniere              1
  Lingue, mercati e culture dell'Asia         0
  SCI                                         0

• Check for L1 but we decided not to filter for it

#Filter by L1

nc <- names(table(all$Context))
table(all$Context)

  English in Germany     English in Italy  German in Australia Italian in Australia 
                  72                   91                   89                   75 

l1_filter <- all[(all$Context == nc[1] & (all$L1 == "German" | all$L1 == "German and English")) | 
                    (all$Context == nc[2] & (all$L1 == "Italian")) | 
                    (all$Context == nc[3] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")) |
                    (all$Context == nc[4] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")),]


#all <- l1_filter

# do not filter for L1
all <- all

# subset demographics
demo <- subset(all,select=c("Resp.ID",demographics_var,l2School_variables))

# Numeri finali
table(l1_filter$Context)

  English in Germany     English in Italy  German in Australia Italian in Australia 
                  65                   87                   78                   73 

table(all$Context)

  English in Germany     English in Italy  German in Australia Italian in Australia 
                  72                   91                   89                   75 

• Filter missing value:
  • Filter participants who didn’t put the degree
  • we don’t care about speak.other.L2 and study.other.L2

missing_bySample <- rowSums(is.na(demo))
names(missing_bySample) <- demo$Resp.ID
missing_byVar <- colSums(is.na(demo))
names(missing_byVar) <- colnames(demo)

barplot(missing_bySample)

d <- data.frame(miss=missing_byVar)
d$varID <- rownames(d)
ggplot(data=d,aes(x=varID,y=miss)) + geom_bar(stat="identity") + theme_bw() +theme(axis.text.x = element_text(angle = 45, hjust = 1)) 

demo_missing <- demo %>% group_by(Context) %>% summarise(roleL2.degree_na = sum(is.na(roleL2.degree)),
                                                         L2.VCE_na = sum(is.na(L2.VCE)),
                                                         other5.other.ways_na=sum(is.na(other5.other.ways )),
                                                         uni1.year_na = sum(is.na(uni1.year)),
                                                         primary1.L2school_na=sum(is.na(primary1.L2school)),
                                                         CLS3.L2school_na = sum(is.na(CLS3.L2school)),
                                                         VSL4.L2school_na=sum(is.na(VSL4.L2school)),
                                                         degree = sum(is.na(degree)),
                                                         schooL2country5.L2school_na=sum(is.na(schooL2country5.L2school)))

# We do not filter for speak.other.L2 or study.other.L2

#demo[is.na(demo$speak.other.L2),]
# teniamo
#demo[is.na(demo$study.other.L2),]
missing_bySample[names(missing_bySample) == "5166861581"]

5166861581 
        10 

#demo[is.na(demo$year.studyL2),]
missing_bySample[names(missing_bySample) == "5378798787"]

5378798787 
         3 

# remove NA from degree
#table(demo$degree,useNA = "always")
# Remove people
all <- all[!is.na(all$degree),]

4.2 Stats about filtered dataset

kable(table(all$Context))

Var1	Freq
English in Germany	70
English in Italy	91
German in Australia	88
Italian in Australia	74

kable(table(all$study.year))

Var1	Freq
1st semester	70
1st year	253

kable(table(all$year.studyL2))

Var1	Freq
0 years	33
1- 3 years	9
1-3 years	7
4-6 years	53
First year of primary school	73
Kindergarten	29
Less than a year	18
more than 6 years	41
Other	59

4.3 Recoded demographic variables

recoded_dem_richi <- read_excel("02-descriptive_data/21 03 merged_filtered_imputedMedian_likertNumber.xlsx")

4.4 Write filtered and merged dataset

write.csv(all,file.path("02-descriptive_data/context-merged_filtered.csv"))

4.5 Descriptive plots and tables

all$speak.other.L2_binary <- ifelse(!is.na(all$speak.other.L2) & 
                                      !(all$speak.other.L2 %in% c("Yes","No")),"Yes",as.character(all$speak.other.L2))


kable(table(all$speak.other.L2_binary,all$Context,useNA = "always"))

	English in Germany	English in Italy	German in Australia	Italian in Australia	NA
No	12	24	52	53	0
Yes	57	67	36	20	0
NA	1	0	0	1	0

NA

• Age

tabAge <- t(table(all$Age,all$Context))
ggdf <- data.frame(Age = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
  N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
  Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])

ggplot(ggdf,aes(x=Age,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by age")+
  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

# add numbers on the bar

tabAge <- t(table(all$Gender,all$Context))
ggdf <- data.frame(Gender = rep(colnames(tabAge),each=4)[!(as.numeric(tabAge) == 0)],
  N.Participants = as.numeric(tabAge)[!(as.numeric(tabAge) == 0)],
  Context = rep(rownames(tabAge),times=3)[!(as.numeric(tabAge) == 0)])


ggplot(ggdf,aes(x=Gender,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + ggtitle("Participants by gender")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

# add numbers on the bar
tabAge <- t(table(all$origins,all$Context))
ggplot(all,aes(x=origins,fill=Context)) + geom_bar(position="dodge",colour="white") + ggtitle("Origins by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=60, width=0.3, height=0.4) + ggtitle("Participants by origins")

tabAge

                      
                       No Yes
  English in Germany   65   5
  English in Italy     90   1
  German in Australia  63  25
  Italian in Australia 36  38

• proficiency

tabAge <- t(table(all$prof,all$Context))
ggplot(all,aes(x=Context,fill=prof)) + geom_bar(position="dodge",colour="white") + ggtitle("Proficiency by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)

tabAge

                      
                       Advanced Elementary Intermediate Upper-intermediate
  English in Germany         38          0            5                 27
  English in Italy           23          2            9                 57
  German in Australia         4         32           25                 27
  Italian in Australia        0         29           29                 16

• L2.VCE

tabAge <- t(table(all[all$Context != "English in Germany" & all$Context != "English in Italy","L2.VCE"],all[all$Context != "English in Germany" & all$Context != "English in Italy",'Context'],useNA = "always"))
tabAge <- tabAge[-3,]

ggplot(all[all$Context != "English in Germany" & all$Context != "English in Italy",],aes(x=Context,fill=L2.VCE)) + geom_bar(position="dodge",colour="white") + ggtitle("L2.VCE by context") + scale_y_continuous(breaks=seq(0,90,10),limits=c(0,90)) + theme_bw() + draw_grob(tableGrob(tabAge), x=2, y=80, width=0.3, height=0.4)

• da mettere a posto con Richi

# year study L2
table(all$year.studyL2,all$other.year.studyL2.richi)

                              
                               BILINGUAL FIRST.YEAR.SECONDARY FOURTH.YEAR.PRIMARY LOWER.SECONDARY PERSONAL SECOND.YEAR.PRIMARY
  0 years                              0                    0                   0               0        0                   0
  1- 3 years                           0                    0                   0               0        0                   0
  1-3 years                            0                    0                   0               0        0                   0
  4-6 years                            0                    0                   0               0        0                   0
  First year of primary school         0                    0                   0               0        0                   0
  Kindergarten                         0                    0                   0               0        0                   0
  Less than a year                     0                    0                   0               0        0                   0
  more than 6 years                    0                    0                   0               0        0                   0
  Other                                4                   10                   5               4        2                   2
                              
                               SECOND.YEAR.SECONDARY THIRD.YEAR.PRIMARY
  0 years                                          0                  0
  1- 3 years                                       0                  0
  1-3 years                                        0                  0
  4-6 years                                        0                  0
  First year of primary school                     0                  0
  Kindergarten                                     0                  0
  Less than a year                                 0                  0
  more than 6 years                                0                  0
  Other                                            2                 28

all$year.studyL2 <- ifelse(all$year.studyL2 == "Other",all$other.year.studyL2.richi,all$year.studyL2 )

# European context
ggplot(all[all$Context == "English in Germany" | all$Context == "English in Italy",],aes(x=degree,fill=year.studyL2)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree by study year L2, by Context") +  facet_grid(~Context,scales="free") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(y = "N participants", x = "degree")

• Degree of enrolment

# Australian context
tabAge <- t(table(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'degree'],all[all$Context == "Italian in Australia" | all$Context == "German in Australia",'Context']))
ggplot(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in Australian Contexts") + draw_grob(tableGrob(tabAge), x=1., y=40, width=0.3, height=0.4)

tabAge

                      
                       HUM HUM.SCI SCI
  German in Australia   48       4  36
  Italian in Australia  50       2  22

# Australian context
tabAge <- t(table(all[all$Context == "English in Italy" | all$Context == "English in Germany",'degree'],all[all$Context == "English in Italy" | all$Context == "English in Germany",'Context']))

ggplot(all[all$Context == "English in Italy" | all$Context == "English in Germany",],aes(x=Context,fill=degree)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree in European Contexts")

tabAge

                    
                     BA in Anglistik BA in Nordamerikastudien LA Lingue e letterature straniere Lingue, mercati e culture dell'Asia
  English in Germany              39                        4 27                              0                                   0
  English in Italy                 0                        0  0                             78                                  13

5 Australian context spcific variables

kable(table(all$reconnect.comm,all$Context))

	English in Germany	English in Italy	German in Australia	Italian in Australia
Agree	0	0	8	11
Disagree	0	0	35	14
Not sure	0	0	3	4
Strongly agree	0	0	12	28
Strongly disagree	0	0	30	17

kable(table(all$speakersmelb.comm,all$Context))

	English in Germany	English in Italy	German in Australia	Italian in Australia
Agree	0	0	44	41
Disagree	0	0	6	2
Not sure	0	0	25	12
Strongly agree	0	0	12	19
Strongly disagree	0	0	1	0

kable(table(all$comecloser.comm,all$Context))

	English in Germany	English in Italy	German in Australia	Italian in Australia
Agree	0	0	21	34
Disagree	0	0	16	6
Not sure	0	0	43	17
Strongly agree	0	0	6	17
Strongly disagree	0	0	2	0

6 Likert scales

• Convert Likert scales to numbers

convertToNumber <- function(column){
  column <- factor(column,levels = c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
  column_number <- as.numeric(column)
  return(column_number)
}

table(all$Context)

  English in Germany     English in Italy  German in Australia Italian in Australia 
                  70                   91                   88                   74 

table(all$study.year)

1st semester     1st year 
          70          253 

convert_likert <- data.frame(apply(subset(all,select=likert_variables_all),2,convertToNumber))
colnames(convert_likert) <- paste0(colnames(convert_likert),"1")

likert_variables1 <- paste0(likert_variables_all,"1")

# join the converted variables to the filtered dataset
filtered_conv <- cbind(all,convert_likert)

table(filtered_conv[,likert_variables_all[4]],filtered_conv[,likert_variables1[4]],useNA = "always")

                   
                      1   2   3   4   5 <NA>
  Agree               0   0   0 121   0    0
  Disagree            0  10   0   0   0    0
  Not sure            0   0  39   0   0    0
  Strongly agree      0   0   0   0 152    0
  Strongly disagree   1   0   0   0   0    0
  <NA>                0   0   0   0   0    0

write.csv(filtered_conv,"02-descriptive_data/merged_filtered_likertNumber.csv",row.names = FALSE)

6.1 Impute missing values - Using median values

The missing values appears to be at random and there are max two missing values in one variable (see plots below). In order not to loose 12 participants while doing the factor analysis across contexts it is preferable to impute the 12 missing values.

all <- filtered_conv

# Items to use for factor analysis : items shared between contexts
# items to be used for the FA

usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]

rownames(all) <- all$Resp.ID
usable_data_context <- all[,c(usable_items,"Context")]
dat_noNA <- usable_data_context[rowSums(is.na(usable_data_context)) == 0,]
all_noNA <- all[rowSums(is.na(usable_data_context)) == 0,]
table(rowSums(is.na(usable_data_context)))

  0   1 
311  12 

# Participants with NA to remove
table(rowSums(is.na(usable_data_context)),usable_data_context$Context,useNA = "always")

      
       English in Germany English in Italy German in Australia Italian in Australia <NA>
  0                    70               85                  87                   69    0
  1                     0                6                   1                    5    0
  <NA>                  0                0                   0                    0    0

# Variable missing values
table(colSums(is.na(usable_data_context)))

 0  1  2 
19 10  1 

table(rowSums(is.na(usable_data_context)),usable_data_context$Context,useNA = "always")

      
       English in Germany English in Italy German in Australia Italian in Australia <NA>
  0                    70               85                  87                   69    0
  1                     0                6                   1                    5    0
  <NA>                  0                0                   0                    0    0

# check what to use to impute
# have a look at the distribution of missing values
library(mice)
library(VIM)

mice_plot <- aggr(usable_data_context[,usable_items], col=c('navyblue','yellow'),
                    numbers=TRUE, sortVars=TRUE,
                    labels=names(usable_data_context[,usable_items]), cex.axis=.4,
                    gap=1, ylab=c("Missing data","Pattern"),cex.numbers=0.5)

 Variables sorted by number of missings: 

# Imputing using median
library(Hmisc)
imputedMedian <- usable_data_context

imputedMedian$globalaccess.post1 <- with(imputedMedian[,usable_items], impute(globalaccess.post1, median))
imputedMedian$citizen.post1 <- with(imputedMedian[,usable_items], impute(citizen.post1, median))
imputedMedian$money.instru1 <- with(imputedMedian[,usable_items], impute(money.instru1, median))
imputedMedian$knowledge.instru1 <- with(imputedMedian[,usable_items], impute(knowledge.instru1, median))
imputedMedian$life.intr1 <- with(imputedMedian[,usable_items], impute(life.intr1, median))
imputedMedian$time.integr1 <- with(imputedMedian[,usable_items], impute(time.integr1, median))
imputedMedian$expect.ought1 <- with(imputedMedian[,usable_items], impute(expect.ought1, median))
imputedMedian$job.instru1 <- with(imputedMedian[,usable_items], impute(job.instru1, median))
imputedMedian$career.instru1 <- with(imputedMedian[,usable_items], impute(career.instru1, median))
imputedMedian$meeting.integr1 <- with(imputedMedian[,usable_items], impute(meeting.integr1, median))
imputedMedian$interact.post1 <- with(imputedMedian[,usable_items], impute(interact.post1, median))

# check before after
table(imputedMedian$time.integr1)

  2   3   4   5 
  3  21  95 204 

table(usable_data_context$time.integr1)

  2   3   4   5 
  3  21  95 202 

table(imputedMedian$life.intr1)

  1   2   3   4   5 
  8  79  81 117  38 

table(usable_data_context$life.intr1)

  1   2   3   4   5 
  8  79  80 117  38 

table(imputedMedian$knowledge.instru1)

  1   2   3   4   5 
  1   2  29 189 102 

table(usable_data_context$knowledge.instru1)

  1   2   3   4   5 
  1   2  29 188 102 

table(imputedMedian$money.instru1)

  1   2   3   4   5 
  3  38 179  84  19 

table(usable_data_context$money.instru1)

  1   2   3   4   5 
  3  38 178  84  19 

table(imputedMedian$citizen.post1)

  1   2   3   4   5 
  3  22  75 148  75 

table(usable_data_context$citizen.post1)

  1   2   3   4   5 
  3  22  75 147  75 

table(imputedMedian$globalaccess.post1)

  1   2   3   4   5 
  1   3  20 159 140 

table(usable_data_context$globalaccess.post1)

  1   2   3   4   5 
  1   3  20 158 140 

table(imputedMedian$expect.ought1)

  1   2   3   4   5 
126 142  30  21   4 

table(usable_data_context$expect.ought1)

  1   2   3   4   5 
126 141  30  21   4 

table(imputedMedian$job.instru1)

  2   3   4   5 
 13 103 133  74 

table(usable_data_context$job.instru1)

  2   3   4   5 
 13 103 132  74 

table(imputedMedian$career.instru1)

  1   2   3   4   5 
  1   1  63 131 127 

table(usable_data_context$career.instru1)

  1   2   3   4   5 
  1   1  63 130 127 

table(imputedMedian$meeting.integr1)

  2   3   4   5 
  1  10 121 191 

table(usable_data_context$meeting.integr1)

  2   3   4   5 
  1  10 121 190 

table(imputedMedian$interact.post1)

  2   3   4   5 
  1  19 140 163 

table(usable_data_context$interact.post1)

  2   3   4   5 
  1  19 140 162 

• Substitute imputed data for the common variables to be used in the Factor Analysis

all <- all[,!(colnames(all) %in% usable_items)]
imputedMedian$Context <- NULL
sum(!(colnames(imputedMedian) %in% usable_items))

[1] 0

all <- cbind(all,imputedMedian[match(rownames(imputedMedian),all$Resp.ID),])

Add some updates that Richi did in Date 7th June 2018

> Other_ways_and_degree_role_with_respondent_IDs <- read_excel("Other-ways-and-degree-role-with-respondent-IDs.xlsx")
> sum(Other_ways_and_degree_role_with_respondent_IDs$Resp.ID != Other_ways_and_degree_role_with_respondent_IDs$Resp.ID__1)
> # to replace 
> # match for the NA degree.role
> 
> match_updates <- match(all$Resp.ID,Other_ways_and_degree_role_with_respondent_IDs$Resp.ID)
> all$private.lessons1.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$private.lessons1.other.ways
> all$study.holiday2.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$study.holiday2.other.ways
> all$year.sem.abroad3.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$year.sem.abroad3.other.ways
> all$online.course4.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$online.course4.other.ways
> all$other5.other.ways[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$other5.other.ways
> all$degree.role[match_updates] <- Other_ways_and_degree_role_with_respondent_IDs$degree.role

6.2 Save imputed data

write.csv(all,"02-descriptive_data/merged_filtered_imputedMedian_likertNumber.csv",row.names = FALSE)

7 Barplot of likert variables

all_melt <- melt(all,id.vars = c("Resp.ID","Gender","Age","prof","Context","study.year"),
                        measure.vars = likert_variables1)

Warning: attributes are not identical across measure variables; they will be dropped

all_melt$value <- factor(all_melt$value,levels=c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
# dim(all_melt)
# 323*length(likert_variables1)

all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 646 rows [9368, 9369, 9370, 9371, 9372, 9373, 9374, 9375, 9376, 9377, 9378, 9379, 9380, 9381, 9382, 9383, 9384, 9385, 9386, 9387, ...].

ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack",colour="black") + 
  facet_grid(Context~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8)) + ggtitle("Filtered dataset") + scale_fill_manual(values=c("#ca0020","#f4a582","#ffffbf","#abd9e9","#2c7bb6","grey"))

filt_sum <- all_melt %>% group_by(Context,variable,type,value) %>% dplyr::summarise(Ngroup=length(value))

`summarise()` has grouped output by 'Context', 'variable', 'type'. You can override using the `.groups` argument.

ggplot(filt_sum,aes(x=value,y=Ngroup,colour=Context,group=interaction(variable, Context))) + geom_line() + geom_point() + facet_wrap(~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1))

7.1 Barplot of Educated and Necessity in the Australian and European Contexts

• Educated

# add numbers on the bar
educated <- all[all$Context %in% c("German in Australia","Italian in Australia"),]
table(educated$educated1,educated$Context,useNA="always")

      
       German in Australia Italian in Australia <NA>
  1                     11                    9    0
  2                     25                   24    0
  3                     12                   13    0
  4                     29                   18    0
  5                     11                   10    0
  <NA>                   0                    0    0

educated$educated1 <- factor(educated$educated1,levels = c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree")) 

tabEdu <- t(table(educated$educated1,educated$Context))
ggdf <- data.frame(Educated = rep(colnames(tabEdu),each=2),
  N.Participants = as.numeric(tabEdu),
  Context = rep(rownames(tabEdu),times=5))


ggplot(ggdf,aes(x=Educated,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Educated by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

ggplot(ggdf,aes(x=Context,y=N.Participants,fill=Educated)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Educated by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

• Necessity

# add numbers on the bar
necessity <- all[all$Context %in% c("English in Germany","English in Italy"),]
table(necessity$necessity1,necessity$Context,useNA="always")

      
       English in Germany English in Italy <NA>
  1                     1               12    0
  2                     7               16    0
  3                    13                6    0
  4                    32               36    0
  5                    16               20    0
  <NA>                  1                1    0

necessity$necessity1 <- factor(necessity$necessity1,levels = c(1,2,3,4,5),labels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree")) 

tabNec <- t(table(necessity$necessity1,necessity$Context,useNA = "always"))[-3,]
ggdf <- data.frame(Necessity = rep(colnames(tabNec),each=2),
  N.Participants = as.numeric(tabNec),
  Context = rep(rownames(tabNec),times=6))


ggplot(ggdf,aes(x=Necessity,y=N.Participants,fill=Context)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,40,10),limits=c(0,40)) + theme_bw() + ggtitle("Necessity by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

ggplot(ggdf,aes(x=Context,y=N.Participants,fill=Necessity)) + geom_bar(position="dodge",colour="white",stat="identity")  + labs(y="N participants") + scale_y_continuous(breaks=seq(0,35,10),limits=c(0,35)) + theme_bw() + ggtitle("Necessity by Context")+  geom_text(aes(label = N.Participants), hjust=0.5, vjust=-0.25, size = 2.5,position=position_dodge(width=0.9)) 

8 Correlation plot of items by context

8.1 Italian in Australia

cov <- cor(filtered_conv[filtered_conv$Context == "Italian in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
data_cor_ita_in_au <- data.frame(cor_ita_in_au=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))

row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

#pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE],  annotation_colors = ann_colors_wide,breaks=seq(-1,1,0.2),col=c("#67001f","#b2182b","#d6604d","#f4a582","#fddbc7","#f7f7f7","#d1e5f0","#92c5de","#4393c3","#2166ac","#053061"),show_colnames = FALSE,width = 7,height = 7)
###################

diag(cov) <- NA
pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

cov_ItaAus <- cov

8.2 German in Australia

cov <- cor(filtered_conv[filtered_conv$Context == "German in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
data_cor_germ_in_au <- data.frame(cor_germ_in_au=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))



row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "German in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

# 
cov_GermAus <- cov

8.3 English in Germany

cov <- cor(filtered_conv[filtered_conv$Context == "English in Germany",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1",    "speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
data_cor_eng_in_germ <- data.frame(cor_eng_in_germ=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))


row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(id1="#f6e8c3",necessity="#b35806",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "English in Germany",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

cov_EngGerm <- cov

8.4 English in Italy

cov <- cor(filtered_conv[filtered_conv$Context == "English in Italy",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1","speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
data_cor_eng_in_ita <- data.frame(cor_eng_in_ita=cov[lower.tri(cov, diag = TRUE)],
                var1 = rownames(cov)[unlist(t(mapply(":", 1:nrow(cov), nrow(cov)))[1,])],
                var2 = rep(colnames(cov),times=rev(seq(nrow(cov):1))))


row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- "necessity"
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",necessity="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "English in Italy",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

# 
cov_EngIta <- cov

8.5 Correlation between correlation in the different contexts

We will perform an exploratory FA combining all the contexts together. This means that we are assuming that the correlation between items across context has the same direction (does not happen that cor(item1,item2)_context1 > 0 and cor(item1,item2)_context2 < 0).

common <- rownames(cov_EngIta)[rownames(cov_EngIta) %in% rownames(cov_ItaAus)]
sum(rownames(cov_EngIta) != rownames(cov_EngGerm))

[1] 0

sum(rownames(cov_EngIta) != rownames(cov_GermAus))

Warning: longer object length is not a multiple of shorter object length

[1] 4

sum(rownames(cov_EngIta) != rownames(cov_ItaAus))

Warning: longer object length is not a multiple of shorter object length

[1] 4

sum(rownames(cov_GermAus) != rownames(cov_ItaAus))

[1] 0

common_EngIta <- cov_EngIta[rownames(cov_EngIta) %in% common,colnames(cov_EngIta) %in% common]
common_EngGerm <- cov_EngGerm[rownames(cov_EngGerm) %in% common,colnames(cov_EngGerm) %in% common]
common_GermAus <- cov_GermAus[rownames(cov_GermAus) %in% common,colnames(cov_GermAus) %in% common]
common_ItaAus <- cov_ItaAus[rownames(cov_ItaAus) %in% common,colnames(cov_ItaAus) %in% common]

sum(rownames(common_EngIta) != colnames(common_EngIta))

[1] 0

sum(rownames(common_EngGerm) != colnames(common_EngGerm))

[1] 0

sum(rownames(common_GermAus) != colnames(common_GermAus))

[1] 0

sum(rownames(common_ItaAus) != colnames(common_ItaAus))

[1] 0

par(mfrow=c(2,3))
plot(common_EngIta,common_EngGerm)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")

abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_GermAus)

abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngIta,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")

abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngGerm,common_GermAus)

abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngGerm,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")

abline(a=0,b=1,lty=2,col = "dark red")

plot(common_GermAus,common_ItaAus)

abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

# Largest differences
low_EngGerm <- lower.tri(common_EngGerm)
common_EngGerm[!low_EngGerm] <- NA
common_GermAus[!(lower.tri(common_GermAus))] <- NA
common_ItaAus[!(lower.tri(common_ItaAus))] <- NA
common_EngIta[!(lower.tri(common_EngIta))] <- NA


mat <- data.frame(common_EngGerm=c(common_EngGerm),
                  common_EngIta=c(common_EngIta),
                  common_GermAus = c(common_GermAus),
                  common_ItaAus = c(common_ItaAus),
                  compare = paste(rownames(common_EngGerm),colnames(common_EngGerm),sep=".")) %>%
  filter(!is.na(common_EngGerm)) %>%
  separate(compare,into=c("item1","variable1","item2","variable2"),remove=FALSE,sep="[.]") %>%
  unite(group,variable1,variable2,sep=".")

library(ggrepel)
ggplot(mat,aes(x=common_EngGerm,y=common_GermAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_ItaAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_EngIta,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

8.6 Different correlation plot and Pearson’s correlation

cowplot::plot_grid(p1,p2,p3,p4,p5,p6,nrow=3)

knitr::kable(cor_data)

	comparison	r_squared
common_EngGerm-common_ItaAus	common_EngGerm-common_ItaAus	0.4538018
common_EngGerm-common_GermAus	common_EngGerm-common_GermAus	0.4757044
common_EngIta-common_GermAus	common_EngIta-common_GermAus	0.5091050
common_EngIta-common_ItaAus	common_EngIta-common_ItaAus	0.5384113
common_EngIta-common_EngGerm	common_EngIta-common_EngGerm	0.5957948
common_GermAus-common_ItaAus	common_GermAus-common_ItaAus	0.5999612

8.7 All context together

cov <- cor(filtered_conv[,likert_variables1],method = "pearson",use="pairwise.complete.obs")

row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
row_infos$Variables <- as.character(row_infos$Variables)
rownames(row_infos) <- rownames(cov)
row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity","educated")
row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]

ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="orange", id1="#f6e8c3",instru1="#35978f",necessity="#b35806",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))

diag(cov) <- NA
pheatmap(cov, main = "All Contexts",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
,  annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))

8.8 Compare correlations

library(GGally)
combine_cor <- merge(data_cor_eng_in_ita,data_cor_eng_in_germ,all = TRUE)
combine_cor1 <- merge(combine_cor,data_cor_germ_in_au,all = TRUE)
combine_cor2 <- merge(combine_cor1,data_cor_ita_in_au,all = TRUE)
combine_cor2 <- combine_cor2 %>% separate(var1,into=c("item1","variable1"),sep="[.]",remove=FALSE) %>%
  separate(var2,into=c("item2","variable2"),sep="[.]",remove=FALSE) %>%
  unite(group,variable1,variable2,sep=".")

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 60 rows [133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, ...].Warning: Expected 2 pieces. Missing pieces filled with `NA` in 5 rows [101, 141, 393, 467, 498].

pairs(combine_cor2[,c("cor_eng_in_ita","cor_eng_in_germ","cor_germ_in_au","cor_ita_in_au")])

ggpairs(combine_cor2[,c("cor_eng_in_ita","cor_eng_in_germ","cor_germ_in_au","cor_ita_in_au")])

8.9 Correlation between correlation in the different contexts

We will perform an exploratory FA combining all the contexts together. This means that we are assuming that the correlation between items across context has the same direction (does not happen that cor(item1,item2)_context1 > 0 and cor(item1,item2)_context2 < 0).

common <- rownames(cov_EngIta)[rownames(cov_EngIta) %in% rownames(cov_ItaAus)]
sum(rownames(cov_EngIta) != rownames(cov_EngGerm))

[1] 0

sum(rownames(cov_EngIta) != rownames(cov_GermAus))

Warning: longer object length is not a multiple of shorter object length

[1] 4

sum(rownames(cov_EngIta) != rownames(cov_ItaAus))

Warning: longer object length is not a multiple of shorter object length

[1] 4

sum(rownames(cov_GermAus) != rownames(cov_ItaAus))

[1] 0

common_EngIta <- cov_EngIta[rownames(cov_EngIta) %in% common,colnames(cov_EngIta) %in% common]
common_EngGerm <- cov_EngGerm[rownames(cov_EngGerm) %in% common,colnames(cov_EngGerm) %in% common]
common_GermAus <- cov_GermAus[rownames(cov_GermAus) %in% common,colnames(cov_GermAus) %in% common]
common_ItaAus <- cov_ItaAus[rownames(cov_ItaAus) %in% common,colnames(cov_ItaAus) %in% common]

sum(rownames(common_EngIta) != colnames(common_EngIta))

[1] 0

sum(rownames(common_EngGerm) != colnames(common_EngGerm))

[1] 0

sum(rownames(common_GermAus) != colnames(common_GermAus))

[1] 0

sum(rownames(common_ItaAus) != colnames(common_ItaAus))

[1] 0

par(mfrow=c(2,3))
plot(common_EngIta,common_EngGerm)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")

abline(a=0,b=1,lty=2,col = "dark red")
plot(common_EngIta,common_GermAus)

abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngIta,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")

abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngGerm,common_GermAus)

abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

plot(common_EngGerm,common_ItaAus)
abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")

abline(a=0,b=1,lty=2,col = "dark red")

plot(common_GermAus,common_ItaAus)

abline(h=c(0,0.3),v=c(0,0.3),lty=2,col = "dark red")
abline(a=0,b=1,lty=2,col = "dark red")

# Largest differences
low_EngGerm <- lower.tri(common_EngGerm)
common_EngGerm[!low_EngGerm] <- NA
common_GermAus[!(lower.tri(common_GermAus))] <- NA
common_ItaAus[!(lower.tri(common_ItaAus))] <- NA
common_EngIta[!(lower.tri(common_EngIta))] <- NA


mat <- data.frame(common_EngGerm=c(common_EngGerm),
                  common_EngIta=c(common_EngIta),
                  common_GermAus = c(common_GermAus),
                  common_ItaAus = c(common_ItaAus),
                  compare = paste(rownames(common_EngGerm),colnames(common_EngGerm),sep=".")) %>%
  filter(!is.na(common_EngGerm)) %>%
  separate(compare,into=c("item1","variable1","item2","variable2"),remove=FALSE,sep="[.]") %>%
  unite(group,variable1,variable2,sep=".")

library(ggrepel)
ggplot(mat,aes(x=common_EngGerm,y=common_GermAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_ItaAus,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

ggplot(mat,aes(x=common_EngGerm,y=common_EngIta,label=compare,colour=group)) + geom_point(alpha=0.5,size=0.8) + geom_text(size=2) +
  geom_hline(yintercept=c(0,0.3),linetype="dotted",colour="dark red") +
geom_vline(xintercept=c(0,0.3),linetype="dotted",colour="dark red")

9 Evaluate internal consistency of known constructs with alpha

sets <- list(id.var=likert_variables1[grep("\\.id1$",likert_variables1)],
             ought.var=likert_variables1[grep("\\.ought1$",likert_variables1)],
             intr.var=likert_variables1[grep("\\.intr1$",likert_variables1)],
             instru.var=likert_variables1[grep("\\.instru1$",likert_variables1)],
             integr1.var=likert_variables1[grep("\\.integr1$",likert_variables1)],
             prof.var=likert_variables1[grep("\\.prof1$",likert_variables1)],
             post.var=likert_variables1[grep("\\.post1$",likert_variables1)],
             comm.var=likert_variables1[grep("\\.comm1$",likert_variables1)])
              

get_alpha <- function(dataMot,
                      var=sets$id.var){
  var_alpha <- psych::alpha(dataMot[,var])
  dataf <- data.frame(alpha=var_alpha$total,
                    drop = var_alpha$alpha.drop)
  rownames(dataf) <- rownames(var_alpha$alpha.drop)
  return(dataf)
}

# "Italian in Australia"
ita_in_au_df <- filtered_conv[filtered_conv$Context == "Italian in Australia",]
list_of_ita_in_au <- lapply(sets,function(x) {
  get_alpha(dataMot=ita_in_au_df,var=x)})
ita_in_au <- do.call(rbind,list_of_ita_in_au)
ita_in_au$var <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[1]) 
ita_in_au$var.full <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[3]) 
ita_in_au$Context <- "Italian in Australia"
rownames(ita_in_au) <- NULL

# "German in Australia"
germ_in_au <- do.call(rbind,lapply(sets,function(x) {
  get_alpha(dataMot =filtered_conv[filtered_conv$Context == "German in Australia",],
                      var=x)}))

Warning: Some items were negatively correlated with the total scale and probably 
should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option

Some items ( knowledge.instru1 ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option

germ_in_au$var <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[1]) 
germ_in_au$var.full <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[3]) 
germ_in_au$Context <- "German in Australia"
rownames(germ_in_au) <- NULL

# "English in Germany"
eng_in_germ <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
  get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Germany",],
                      var=x)}))

Warning: Some items were negatively correlated with the total scale and probably 
should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option

Some items ( people.ought1 ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option

# the ones that makes issues
get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Germany",],
                      var=sets$ought.var)

Warning: Some items were negatively correlated with the total scale and probably 
should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option

Some items ( people.ought1 ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option

eng_in_germ$var <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[1]) 
eng_in_germ$var.full <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[3]) 
eng_in_germ$Context <- "English in Germany"
rownames(eng_in_germ) <- NULL

# "English in Italy"
eng_in_ita <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
  get_alpha(dataMot=filtered_conv[filtered_conv$Context == "English in Italy",],
                      var=x)}))
eng_in_ita$var <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[1]) 
eng_in_ita$var.full <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[3]) 
eng_in_ita$Context <- "English in Italy"
rownames(eng_in_ita) <- NULL


# combine
full_alpha <- rbind(eng_in_ita,eng_in_germ,germ_in_au,ita_in_au)

• Plot alpha by variable

full_alpha %>% group_by(Context,var) %>% 
  summarise(st.alpha = unique(alpha.std.alpha),
            G6=unique(alpha.G6.smc.)) %>%
  ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw()

`summarise()` has grouped output by 'Context'. You can override using the `.groups` argument.

p2=ggplot(full_alpha,aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")

Error in ggplot(full_alpha, aes(x = var.full, y = drop.std.alpha, colour = Context)) : 
  object 'full_alpha' not found

```r
ggplot(full_alpha[full_alpha$var == \instru\,],aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + labs(y = \Drop Item - Chronbach's Alpha\, x = \Items\)

```